Overview

Dataset statistics

Number of variables44
Number of observations124
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory42.8 KiB
Average record size in memory353.0 B

Variable types

Numeric21
Categorical12
Boolean10
Unsupported1

Alerts

AMTS status has constant value "normal cognitive function" Constant
presence of any etiologic criteria (GLIM ) has constant value "True" Constant
number of chronic diseases is highly correlated with number of regular drugs and 3 other fieldsHigh correlation
number of regular drugs is highly correlated with number of chronic diseases and 3 other fieldsHigh correlation
FEV1/FVC EX is highly correlated with FEV1 and 1 other fieldsHigh correlation
FEV1 is highly correlated with age and 3 other fieldsHigh correlation
height (cm) is highly correlated with gender and 9 other fieldsHigh correlation
weight (kg) is highly correlated with number of regular drugs and 18 other fieldsHigh correlation
BFM (Body Fat Mass) is highly correlated with presence of diabetes  and 14 other fieldsHigh correlation
SMM (Skeletal Muscle Mass) is highly correlated with gender and 15 other fieldsHigh correlation
BMI (Body Mass Index) is highly correlated with presence of diabetes  and 16 other fieldsHigh correlation
PBF (Percent Body Fat) is highly correlated with weight (kg) and 3 other fieldsHigh correlation
FFM of Right Arm is highly correlated with gender and 13 other fieldsHigh correlation
FFM of Left Arm is highly correlated with gender and 14 other fieldsHigh correlation
FFM of Right Leg is highly correlated with gender and 12 other fieldsHigh correlation
FFM of Left Leg is highly correlated with gender and 12 other fieldsHigh correlation
FFM of Trunk is highly correlated with gender and 14 other fieldsHigh correlation
MNA- SF (points) is highly correlated with MNA-SF status and 6 other fieldsHigh correlation
MUST (points) is highly correlated with weight (kg) and 9 other fieldsHigh correlation
ALM index is highly correlated with gender and 17 other fieldsHigh correlation
handgrip strenght (mean value for two hands) is highly correlated with gender and 6 other fieldsHigh correlation
presence of any phenotypic criteria (GLIM ) is highly correlated with weight (kg) and 9 other fieldsHigh correlation
sarcopenia confirmed according to EWGSOP2 is highly correlated with weight (kg) and 10 other fieldsHigh correlation
NRS-2002 (points) is highly correlated with NRS-2002 statusHigh correlation
low handgrip strenght <27 kg for men and <16 kg for women is highly correlated with handgrip strenght (mean value for two hands) and 1 other fieldsHigh correlation
MUST status is highly correlated with weight (kg) and 13 other fieldsHigh correlation
place of living is highly correlated with presence of any etiologic criteria (GLIM ) and 1 other fieldsHigh correlation
weak legs- Chair Stand Test >15 seconds is highly correlated with Chair Stand Test (s) High correlation
presence of any etiologic criteria (GLIM ) is highly correlated with presence of any phenotypic criteria (GLIM ) and 20 other fieldsHigh correlation
level of education is highly correlated with age High correlation
NRS-2002 status is highly correlated with NRS-2002 (points)High correlation
living condition is highly correlated with presence of any etiologic criteria (GLIM ) and 1 other fieldsHigh correlation
presence of cardiovascular disease is highly correlated with number of chronic diseasesHigh correlation
presence of hypertension is highly correlated with number of chronic diseasesHigh correlation
gender is highly correlated with subject number and 9 other fieldsHigh correlation
presence of diabetes  is highly correlated with number of chronic diseases and 2 other fieldsHigh correlation
Low muscle mass (ALM index <=7.40 for men and <=5.6 for women; polish cut-off points) is highly correlated with weight (kg) and 16 other fieldsHigh correlation
AMTS (points) is highly correlated with presence of any etiologic criteria (GLIM ) and 1 other fieldsHigh correlation
Severity of COPD according to the GOLD classification is highly correlated with FEV1/FVC EX and 1 other fieldsHigh correlation
AMTS status is highly correlated with presence of any phenotypic criteria (GLIM ) and 20 other fieldsHigh correlation
MNA-SF status is highly correlated with weight (kg) and 12 other fieldsHigh correlation
presence of dyslipidemia is highly correlated with presence of any etiologic criteria (GLIM ) and 1 other fieldsHigh correlation
subject number is highly correlated with genderHigh correlation
age is highly correlated with level of education and 1 other fieldsHigh correlation
Chair Stand Test (s) is highly correlated with MNA- SF (points) and 1 other fieldsHigh correlation
subject number is uniformly distributed Uniform
subject number has unique values Unique
6MWT results is an unsupported type, check if it needs cleaning or further analysis Unsupported

Reproduction

Analysis started2022-09-19 20:20:31.182544
Analysis finished2022-09-19 20:22:15.917267
Duration1 minute and 44.73 seconds
Software versionpandas-profiling v3.3.0
Download configurationconfig.json

Variables

subject number
Real number (ℝ≥0)

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct124
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.5
Minimum1
Maximum124
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2022-09-19T22:22:16.242534image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7.15
Q131.75
median62.5
Q393.25
95-th percentile117.85
Maximum124
Range123
Interquartile range (IQR)61.5

Descriptive statistics

Standard deviation35.93976442
Coefficient of variation (CV)0.5750362307
Kurtosis-1.2
Mean62.5
Median Absolute Deviation (MAD)31
Skewness0
Sum7750
Variance1291.666667
MonotonicityStrictly increasing
2022-09-19T22:22:16.648365image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11
 
0.8%
791
 
0.8%
921
 
0.8%
911
 
0.8%
901
 
0.8%
891
 
0.8%
881
 
0.8%
871
 
0.8%
861
 
0.8%
851
 
0.8%
Other values (114)114
91.9%
ValueCountFrequency (%)
11
0.8%
21
0.8%
31
0.8%
41
0.8%
51
0.8%
61
0.8%
71
0.8%
81
0.8%
91
0.8%
101
0.8%
ValueCountFrequency (%)
1241
0.8%
1231
0.8%
1221
0.8%
1211
0.8%
1201
0.8%
1191
0.8%
1181
0.8%
1171
0.8%
1161
0.8%
1151
0.8%

age
Real number (ℝ≥0)

HIGH CORRELATION

Distinct25
Distinct (%)20.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.37903226
Minimum60
Maximum86
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2022-09-19T22:22:17.100961image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum60
5-th percentile60.15
Q165
median69
Q373
95-th percentile81
Maximum86
Range26
Interquartile range (IQR)8

Descriptive statistics

Standard deviation6.052067767
Coefficient of variation (CV)0.08723194271
Kurtosis-0.2040829994
Mean69.37903226
Median Absolute Deviation (MAD)4
Skewness0.5723351367
Sum8603
Variance36.62752426
MonotonicityNot monotonic
2022-09-19T22:22:17.591237image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
7111
 
8.9%
6711
 
8.9%
6910
 
8.1%
689
 
7.3%
658
 
6.5%
607
 
5.6%
707
 
5.6%
667
 
5.6%
626
 
4.8%
735
 
4.0%
Other values (15)43
34.7%
ValueCountFrequency (%)
607
5.6%
614
 
3.2%
626
4.8%
634
 
3.2%
645
4.0%
658
6.5%
667
5.6%
6711
8.9%
689
7.3%
6910
8.1%
ValueCountFrequency (%)
861
 
0.8%
841
 
0.8%
823
2.4%
813
2.4%
804
3.2%
791
 
0.8%
781
 
0.8%
775
4.0%
762
 
1.6%
753
2.4%

gender
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
M
74 
W
50 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters124
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowM
3rd rowM
4th rowM
5th rowM

Common Values

ValueCountFrequency (%)
M74
59.7%
W50
40.3%

Length

2022-09-19T22:22:17.951413image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-19T22:22:18.496067image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
m74
59.7%
w50
40.3%

Most occurring characters

ValueCountFrequency (%)
M74
59.7%
W50
40.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter124
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M74
59.7%
W50
40.3%

Most occurring scripts

ValueCountFrequency (%)
Latin124
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
M74
59.7%
W50
40.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII124
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M74
59.7%
W50
40.3%

place of living
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
urban areas
81 
rural areas
43 

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters1,364
Distinct characters9
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowrural areas
2nd rowurban areas
3rd rowurban areas
4th rowrural areas
5th rowurban areas

Common Values

ValueCountFrequency (%)
urban areas81
65.3%
rural areas43
34.7%

Length

2022-09-19T22:22:19.012631image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-19T22:22:19.394872image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
areas124
50.0%
urban81
32.7%
rural43
 
17.3%

Most occurring characters

ValueCountFrequency (%)
a372
27.3%
r291
21.3%
u124
 
9.1%
124
 
9.1%
e124
 
9.1%
s124
 
9.1%
b81
 
5.9%
n81
 
5.9%
l43
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1240
90.9%
Space Separator124
 
9.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a372
30.0%
r291
23.5%
u124
 
10.0%
e124
 
10.0%
s124
 
10.0%
b81
 
6.5%
n81
 
6.5%
l43
 
3.5%
Space Separator
ValueCountFrequency (%)
124
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1240
90.9%
Common124
 
9.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a372
30.0%
r291
23.5%
u124
 
10.0%
e124
 
10.0%
s124
 
10.0%
b81
 
6.5%
n81
 
6.5%
l43
 
3.5%
Common
ValueCountFrequency (%)
124
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1364
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a372
27.3%
r291
21.3%
u124
 
9.1%
124
 
9.1%
e124
 
9.1%
s124
 
9.1%
b81
 
5.9%
n81
 
5.9%
l43
 
3.2%

living condition
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
living with other
99 
living alone
25 

Length

Max length17
Median length17
Mean length15.99193548
Min length12

Characters and Unicode

Total characters1,983
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowliving with other
2nd rowliving with other
3rd rowliving with other
4th rowliving with other
5th rowliving with other

Common Values

ValueCountFrequency (%)
living with other99
79.8%
living alone25
 
20.2%

Length

2022-09-19T22:22:19.794426image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-19T22:22:20.265588image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
living124
35.7%
with99
28.5%
other99
28.5%
alone25
 
7.2%

Most occurring characters

ValueCountFrequency (%)
i347
17.5%
223
11.2%
t198
10.0%
h198
10.0%
l149
7.5%
n149
7.5%
v124
 
6.3%
g124
 
6.3%
o124
 
6.3%
e124
 
6.3%
Other values (3)223
11.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1760
88.8%
Space Separator223
 
11.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i347
19.7%
t198
11.2%
h198
11.2%
l149
8.5%
n149
8.5%
v124
 
7.0%
g124
 
7.0%
o124
 
7.0%
e124
 
7.0%
w99
 
5.6%
Other values (2)124
 
7.0%
Space Separator
ValueCountFrequency (%)
223
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1760
88.8%
Common223
 
11.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
i347
19.7%
t198
11.2%
h198
11.2%
l149
8.5%
n149
8.5%
v124
 
7.0%
g124
 
7.0%
o124
 
7.0%
e124
 
7.0%
w99
 
5.6%
Other values (2)124
 
7.0%
Common
ValueCountFrequency (%)
223
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1983
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i347
17.5%
223
11.2%
t198
10.0%
h198
10.0%
l149
7.5%
n149
7.5%
v124
 
6.3%
g124
 
6.3%
o124
 
6.3%
e124
 
6.3%
Other values (3)223
11.2%

level of education
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
vocational
56 
secondary
44 
higher
12 
primary
10 
no education
 
2

Length

Max length12
Median length10
Mean length9.048387097
Min length6

Characters and Unicode

Total characters1,122
Distinct characters19
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowvocational
2nd rowsecondary
3rd rowvocational
4th rowprimary
5th rowvocational

Common Values

ValueCountFrequency (%)
vocational56
45.2%
secondary44
35.5%
higher12
 
9.7%
primary10
 
8.1%
no education2
 
1.6%

Length

2022-09-19T22:22:20.824113image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-19T22:22:21.506201image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
vocational56
44.4%
secondary44
34.9%
higher12
 
9.5%
primary10
 
7.9%
no2
 
1.6%
education2
 
1.6%

Most occurring characters

ValueCountFrequency (%)
a168
15.0%
o160
14.3%
n104
9.3%
c102
9.1%
i80
 
7.1%
r76
 
6.8%
e58
 
5.2%
t58
 
5.2%
v56
 
5.0%
l56
 
5.0%
Other values (9)204
18.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1120
99.8%
Space Separator2
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a168
15.0%
o160
14.3%
n104
9.3%
c102
9.1%
i80
 
7.1%
r76
 
6.8%
e58
 
5.2%
t58
 
5.2%
v56
 
5.0%
l56
 
5.0%
Other values (8)202
18.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1120
99.8%
Common2
 
0.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a168
15.0%
o160
14.3%
n104
9.3%
c102
9.1%
i80
 
7.1%
r76
 
6.8%
e58
 
5.2%
t58
 
5.2%
v56
 
5.0%
l56
 
5.0%
Other values (8)202
18.0%
Common
ValueCountFrequency (%)
2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1122
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a168
15.0%
o160
14.3%
n104
9.3%
c102
9.1%
i80
 
7.1%
r76
 
6.8%
e58
 
5.2%
t58
 
5.2%
v56
 
5.0%
l56
 
5.0%
Other values (9)204
18.2%

number of chronic diseases
Real number (ℝ≥0)

HIGH CORRELATION

Distinct6
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.137096774
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2022-09-19T22:22:21.998865image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.15
Q12
median3
Q34
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.177945555
Coefficient of variation (CV)0.3754890715
Kurtosis0.4531451639
Mean3.137096774
Median Absolute Deviation (MAD)1
Skewness0.6091398825
Sum389
Variance1.38755573
MonotonicityNot monotonic
2022-09-19T22:22:22.356394image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
348
38.7%
229
23.4%
428
22.6%
68
 
6.5%
17
 
5.6%
54
 
3.2%
ValueCountFrequency (%)
17
 
5.6%
229
23.4%
348
38.7%
428
22.6%
54
 
3.2%
68
 
6.5%
ValueCountFrequency (%)
68
 
6.5%
54
 
3.2%
428
22.6%
348
38.7%
229
23.4%
17
 
5.6%

number of regular drugs
Real number (ℝ≥0)

HIGH CORRELATION

Distinct17
Distinct (%)13.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.298387097
Minimum1
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2022-09-19T22:22:22.761644image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q15
median8
Q311.25
95-th percentile14.85
Maximum20
Range19
Interquartile range (IQR)6.25

Descriptive statistics

Standard deviation4.182775052
Coefficient of variation (CV)0.5040467506
Kurtosis-0.2271767649
Mean8.298387097
Median Absolute Deviation (MAD)3
Skewness0.4007860707
Sum1029
Variance17.49560713
MonotonicityNot monotonic
2022-09-19T22:22:23.393358image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
1214
11.3%
614
11.3%
811
8.9%
79
 
7.3%
119
 
7.3%
49
 
7.3%
109
 
7.3%
58
 
6.5%
28
 
6.5%
148
 
6.5%
Other values (7)25
20.2%
ValueCountFrequency (%)
12
 
1.6%
28
6.5%
37
5.6%
49
7.3%
58
6.5%
614
11.3%
79
7.3%
811
8.9%
97
5.6%
109
7.3%
ValueCountFrequency (%)
202
 
1.6%
182
 
1.6%
153
 
2.4%
148
6.5%
132
 
1.6%
1214
11.3%
119
7.3%
109
7.3%
97
5.6%
811
8.9%

presence of hypertension
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size252.0 B
True
78 
False
46 
ValueCountFrequency (%)
True78
62.9%
False46
37.1%
2022-09-19T22:22:23.797553image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

presence of cardiovascular disease
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size252.0 B
False
88 
True
36 
ValueCountFrequency (%)
False88
71.0%
True36
29.0%
2022-09-19T22:22:24.163586image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

presence of dyslipidemia
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size252.0 B
False
96 
True
28 
ValueCountFrequency (%)
False96
77.4%
True28
 
22.6%
2022-09-19T22:22:24.493185image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

presence of diabetes 
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size252.0 B
False
86 
True
38 
ValueCountFrequency (%)
False86
69.4%
True38
30.6%
2022-09-19T22:22:24.906047image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

AMTS (points)
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
10
63 
9
46 
11
8
 
6

Length

Max length2
Median length2
Mean length1.580645161
Min length1

Characters and Unicode

Total characters196
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row9
3rd row9
4th row9
5th row10

Common Values

ValueCountFrequency (%)
1063
50.8%
946
37.1%
119
 
7.3%
86
 
4.8%

Length

2022-09-19T22:22:25.238805image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-19T22:22:25.654035image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
1063
50.8%
946
37.1%
119
 
7.3%
86
 
4.8%

Most occurring characters

ValueCountFrequency (%)
181
41.3%
063
32.1%
946
23.5%
86
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number196
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
181
41.3%
063
32.1%
946
23.5%
86
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
Common196
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
181
41.3%
063
32.1%
946
23.5%
86
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII196
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
181
41.3%
063
32.1%
946
23.5%
86
 
3.1%

AMTS status
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
normal cognitive function
124 

Length

Max length25
Median length25
Mean length25
Min length25

Characters and Unicode

Total characters3,100
Distinct characters15
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownormal cognitive function
2nd rownormal cognitive function
3rd rownormal cognitive function
4th rownormal cognitive function
5th rownormal cognitive function

Common Values

ValueCountFrequency (%)
normal cognitive function124
100.0%

Length

2022-09-19T22:22:25.942674image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-19T22:22:26.370205image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
normal124
33.3%
cognitive124
33.3%
function124
33.3%

Most occurring characters

ValueCountFrequency (%)
n496
16.0%
o372
12.0%
i372
12.0%
248
 
8.0%
c248
 
8.0%
t248
 
8.0%
r124
 
4.0%
m124
 
4.0%
a124
 
4.0%
l124
 
4.0%
Other values (5)620
20.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2852
92.0%
Space Separator248
 
8.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n496
17.4%
o372
13.0%
i372
13.0%
c248
8.7%
t248
8.7%
r124
 
4.3%
m124
 
4.3%
a124
 
4.3%
l124
 
4.3%
g124
 
4.3%
Other values (4)496
17.4%
Space Separator
ValueCountFrequency (%)
248
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2852
92.0%
Common248
 
8.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n496
17.4%
o372
13.0%
i372
13.0%
c248
8.7%
t248
8.7%
r124
 
4.3%
m124
 
4.3%
a124
 
4.3%
l124
 
4.3%
g124
 
4.3%
Other values (4)496
17.4%
Common
ValueCountFrequency (%)
248
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII3100
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n496
16.0%
o372
12.0%
i372
12.0%
248
 
8.0%
c248
 
8.0%
t248
 
8.0%
r124
 
4.0%
m124
 
4.0%
a124
 
4.0%
l124
 
4.0%
Other values (5)620
20.0%

FEV1/FVC EX
Real number (ℝ≥0)

HIGH CORRELATION

Distinct123
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.9916129
Minimum16.62
Maximum70.38
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2022-09-19T22:22:27.055103image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum16.62
5-th percentile29.262
Q141.1825
median52.68
Q361.9725
95-th percentile69.1955
Maximum70.38
Range53.76
Interquartile range (IQR)20.79

Descriptive statistics

Standard deviation12.64585966
Coefficient of variation (CV)0.2479988166
Kurtosis-0.7405630601
Mean50.9916129
Median Absolute Deviation (MAD)10.28
Skewness-0.356580712
Sum6322.96
Variance159.9177665
MonotonicityNot monotonic
2022-09-19T22:22:27.563797image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
63.642
 
1.6%
52.141
 
0.8%
59.071
 
0.8%
41.611
 
0.8%
68.071
 
0.8%
41.21
 
0.8%
46.211
 
0.8%
54.921
 
0.8%
43.221
 
0.8%
68.311
 
0.8%
Other values (113)113
91.1%
ValueCountFrequency (%)
16.621
0.8%
25.161
0.8%
28.131
0.8%
28.371
0.8%
28.571
0.8%
28.841
0.8%
29.221
0.8%
29.51
0.8%
29.521
0.8%
29.831
0.8%
ValueCountFrequency (%)
70.381
0.8%
701
0.8%
69.891
0.8%
69.591
0.8%
69.521
0.8%
69.361
0.8%
69.21
0.8%
69.171
0.8%
68.311
0.8%
68.151
0.8%

FEV1
Real number (ℝ≥0)

HIGH CORRELATION

Distinct59
Distinct (%)47.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.64516129
Minimum14
Maximum105
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2022-09-19T22:22:28.084502image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile25
Q136.75
median46
Q363
95-th percentile85.1
Maximum105
Range91
Interquartile range (IQR)26.25

Descriptive statistics

Standard deviation18.80848863
Coefficient of variation (CV)0.3713778011
Kurtosis-0.07325435446
Mean50.64516129
Median Absolute Deviation (MAD)13.5
Skewness0.6211199829
Sum6280
Variance353.7592447
MonotonicityNot monotonic
2022-09-19T22:22:28.546739image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
366
 
4.8%
416
 
4.8%
394
 
3.2%
634
 
3.2%
514
 
3.2%
664
 
3.2%
424
 
3.2%
384
 
3.2%
323
 
2.4%
303
 
2.4%
Other values (49)82
66.1%
ValueCountFrequency (%)
141
 
0.8%
201
 
0.8%
231
 
0.8%
243
2.4%
252
1.6%
272
1.6%
281
 
0.8%
291
 
0.8%
303
2.4%
313
2.4%
ValueCountFrequency (%)
1051
0.8%
981
0.8%
972
1.6%
911
0.8%
871
0.8%
861
0.8%
802
1.6%
791
0.8%
781
0.8%
761
0.8%
Distinct4
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
severe obstruction
52 
moderate obstruction
51 
very severe obstruction
12 
mild obstruction

Length

Max length23
Median length20
Mean length19.16129032
Min length16

Characters and Unicode

Total characters2,376
Distinct characters17
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowvery severe obstruction
2nd rowmoderate obstruction
3rd rowsevere obstruction
4th rowmoderate obstruction
5th rowmoderate obstruction

Common Values

ValueCountFrequency (%)
severe obstruction52
41.9%
moderate obstruction51
41.1%
very severe obstruction12
 
9.7%
mild obstruction9
 
7.3%

Length

2022-09-19T22:22:29.186305image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-19T22:22:29.682933image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
obstruction124
47.7%
severe64
24.6%
moderate51
19.6%
very12
 
4.6%
mild9
 
3.5%

Most occurring characters

ValueCountFrequency (%)
e306
12.9%
o299
12.6%
t299
12.6%
r251
10.6%
s188
7.9%
136
 
5.7%
i133
 
5.6%
c124
 
5.2%
n124
 
5.2%
u124
 
5.2%
Other values (7)392
16.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2240
94.3%
Space Separator136
 
5.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e306
13.7%
o299
13.3%
t299
13.3%
r251
11.2%
s188
8.4%
i133
5.9%
c124
5.5%
n124
5.5%
u124
5.5%
b124
5.5%
Other values (6)268
12.0%
Space Separator
ValueCountFrequency (%)
136
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2240
94.3%
Common136
 
5.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e306
13.7%
o299
13.3%
t299
13.3%
r251
11.2%
s188
8.4%
i133
5.9%
c124
5.5%
n124
5.5%
u124
5.5%
b124
5.5%
Other values (6)268
12.0%
Common
ValueCountFrequency (%)
136
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2376
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e306
12.9%
o299
12.6%
t299
12.6%
r251
10.6%
s188
7.9%
136
 
5.7%
i133
 
5.6%
c124
 
5.2%
n124
 
5.2%
u124
 
5.2%
Other values (7)392
16.5%

6MWT results
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size1.1 KiB

height (cm)
Real number (ℝ≥0)

HIGH CORRELATION

Distinct35
Distinct (%)28.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean166.4354839
Minimum142
Maximum189
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2022-09-19T22:22:30.054678image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum142
5-th percentile150
Q1160.75
median167
Q3174
95-th percentile178
Maximum189
Range47
Interquartile range (IQR)13.25

Descriptive statistics

Standard deviation9.259793265
Coefficient of variation (CV)0.05563593201
Kurtosis-0.3576683629
Mean166.4354839
Median Absolute Deviation (MAD)7
Skewness-0.2568475081
Sum20638
Variance85.74377131
MonotonicityNot monotonic
2022-09-19T22:22:30.531758image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
17510
 
8.1%
1649
 
7.3%
1658
 
6.5%
1627
 
5.6%
1727
 
5.6%
1766
 
4.8%
1606
 
4.8%
1706
 
4.8%
1505
 
4.0%
1785
 
4.0%
Other values (25)55
44.4%
ValueCountFrequency (%)
1421
 
0.8%
1461
 
0.8%
1481
 
0.8%
1505
4.0%
1511
 
0.8%
1523
2.4%
1533
2.4%
1542
 
1.6%
1551
 
0.8%
1563
2.4%
ValueCountFrequency (%)
1891
 
0.8%
1852
 
1.6%
1841
 
0.8%
1811
 
0.8%
1801
 
0.8%
1785
4.0%
1771
 
0.8%
1766
4.8%
17510
8.1%
1744
 
3.2%

weight (kg)
Real number (ℝ≥0)

HIGH CORRELATION

Distinct110
Distinct (%)88.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77.97016129
Minimum37.9
Maximum135.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2022-09-19T22:22:31.064655image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum37.9
5-th percentile46.305
Q164
median76.45
Q392.9
95-th percentile114
Maximum135.5
Range97.6
Interquartile range (IQR)28.9

Descriptive statistics

Standard deviation20.5761484
Coefficient of variation (CV)0.2638977278
Kurtosis0.2948995506
Mean77.97016129
Median Absolute Deviation (MAD)13.85
Skewness0.4922130829
Sum9668.3
Variance423.3778829
MonotonicityNot monotonic
2022-09-19T22:22:31.512537image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
71.93
 
2.4%
642
 
1.6%
94.52
 
1.6%
85.82
 
1.6%
83.22
 
1.6%
93.72
 
1.6%
94.32
 
1.6%
81.52
 
1.6%
1142
 
1.6%
94.82
 
1.6%
Other values (100)103
83.1%
ValueCountFrequency (%)
37.91
0.8%
40.21
0.8%
40.41
0.8%
41.21
0.8%
43.71
0.8%
461
0.8%
46.21
0.8%
46.91
0.8%
49.41
0.8%
49.51
0.8%
ValueCountFrequency (%)
135.51
0.8%
133.71
0.8%
129.61
0.8%
129.31
0.8%
127.91
0.8%
119.51
0.8%
1142
1.6%
107.31
0.8%
107.21
0.8%
1071
0.8%

BFM (Body Fat Mass)
Real number (ℝ≥0)

HIGH CORRELATION

Distinct116
Distinct (%)93.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.19274194
Minimum3.6
Maximum61
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2022-09-19T22:22:31.969845image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum3.6
5-th percentile8.115
Q115.825
median25.25
Q334.925
95-th percentile50.695
Maximum61
Range57.4
Interquartile range (IQR)19.1

Descriptive statistics

Standard deviation12.81587667
Coefficient of variation (CV)0.4892911442
Kurtosis-0.1319994162
Mean26.19274194
Median Absolute Deviation (MAD)9.6
Skewness0.564350736
Sum3247.9
Variance164.2466949
MonotonicityNot monotonic
2022-09-19T22:22:32.465982image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.42
 
1.6%
28.82
 
1.6%
33.82
 
1.6%
35.72
 
1.6%
27.92
 
1.6%
33.72
 
1.6%
40.52
 
1.6%
15.52
 
1.6%
38.51
 
0.8%
23.41
 
0.8%
Other values (106)106
85.5%
ValueCountFrequency (%)
3.61
0.8%
6.71
0.8%
6.81
0.8%
7.41
0.8%
7.51
0.8%
7.81
0.8%
8.11
0.8%
8.21
0.8%
8.71
0.8%
8.81
0.8%
ValueCountFrequency (%)
611
0.8%
58.81
0.8%
58.31
0.8%
561
0.8%
53.91
0.8%
53.11
0.8%
50.81
0.8%
50.11
0.8%
471
0.8%
44.81
0.8%

SMM (Skeletal Muscle Mass)
Real number (ℝ≥0)

HIGH CORRELATION

Distinct100
Distinct (%)80.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.5
Minimum16.5
Maximum44.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2022-09-19T22:22:32.975581image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum16.5
5-th percentile18.43
Q122.8
median28.85
Q332.9
95-th percentile39.665
Maximum44.8
Range28.3
Interquartile range (IQR)10.1

Descriptive statistics

Standard deviation6.756135423
Coefficient of variation (CV)0.2370573833
Kurtosis-0.6668397037
Mean28.5
Median Absolute Deviation (MAD)5.35
Skewness0.2153887945
Sum3534
Variance45.64536585
MonotonicityNot monotonic
2022-09-19T22:22:33.437395image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31.34
 
3.2%
233
 
2.4%
33.62
 
1.6%
32.82
 
1.6%
24.22
 
1.6%
22.82
 
1.6%
26.62
 
1.6%
192
 
1.6%
38.42
 
1.6%
32.92
 
1.6%
Other values (90)101
81.5%
ValueCountFrequency (%)
16.51
0.8%
171
0.8%
17.31
0.8%
17.71
0.8%
181
0.8%
18.21
0.8%
18.41
0.8%
18.61
0.8%
18.71
0.8%
18.91
0.8%
ValueCountFrequency (%)
44.81
0.8%
43.91
0.8%
431
0.8%
42.81
0.8%
42.11
0.8%
39.91
0.8%
39.81
0.8%
38.91
0.8%
38.42
1.6%
37.91
0.8%

BMI (Body Mass Index)
Real number (ℝ≥0)

HIGH CORRELATION

Distinct99
Distinct (%)79.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.025
Minimum15.2
Maximum47.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2022-09-19T22:22:33.894578image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum15.2
5-th percentile18.745
Q123.575
median27.35
Q332.175
95-th percentile40.325
Maximum47.6
Range32.4
Interquartile range (IQR)8.6

Descriptive statistics

Standard deviation6.63897858
Coefficient of variation (CV)0.2368948646
Kurtosis0.1950257303
Mean28.025
Median Absolute Deviation (MAD)4.6
Skewness0.5395707966
Sum3475.1
Variance44.07603659
MonotonicityNot monotonic
2022-09-19T22:22:34.597564image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28.84
 
3.2%
24.53
 
2.4%
32.13
 
2.4%
26.83
 
2.4%
25.12
 
1.6%
28.52
 
1.6%
28.32
 
1.6%
34.82
 
1.6%
25.52
 
1.6%
23.72
 
1.6%
Other values (89)99
79.8%
ValueCountFrequency (%)
15.21
0.8%
15.31
0.8%
15.71
0.8%
17.21
0.8%
17.61
0.8%
18.41
0.8%
18.71
0.8%
191
0.8%
19.11
0.8%
19.62
1.6%
ValueCountFrequency (%)
47.61
0.8%
45.31
0.8%
44.21
0.8%
43.72
1.6%
40.91
0.8%
40.41
0.8%
39.91
0.8%
38.11
0.8%
37.91
0.8%
37.71
0.8%

PBF (Percent Body Fat)
Real number (ℝ≥0)

HIGH CORRELATION

Distinct99
Distinct (%)79.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.12580645
Minimum9.4
Maximum50.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2022-09-19T22:22:35.301618image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum9.4
5-th percentile16.305
Q124.65
median33
Q340.05
95-th percentile46.955
Maximum50.2
Range40.8
Interquartile range (IQR)15.4

Descriptive statistics

Standard deviation9.925395456
Coefficient of variation (CV)0.3089539704
Kurtosis-0.9023242192
Mean32.12580645
Median Absolute Deviation (MAD)7.8
Skewness-0.1820090999
Sum3983.6
Variance98.51347495
MonotonicityNot monotonic
2022-09-19T22:22:36.242243image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39.83
 
2.4%
37.93
 
2.4%
44.53
 
2.4%
50.22
 
1.6%
19.92
 
1.6%
40.92
 
1.6%
20.22
 
1.6%
35.92
 
1.6%
16.92
 
1.6%
28.62
 
1.6%
Other values (89)101
81.5%
ValueCountFrequency (%)
9.41
0.8%
12.71
0.8%
13.11
0.8%
14.71
0.8%
15.41
0.8%
15.61
0.8%
16.21
0.8%
16.92
1.6%
17.22
1.6%
18.41
0.8%
ValueCountFrequency (%)
50.22
1.6%
48.71
0.8%
48.51
0.8%
48.41
0.8%
48.11
0.8%
471
0.8%
46.71
0.8%
46.31
0.8%
461
0.8%
45.61
0.8%

FFM of Right Arm
Real number (ℝ≥0)

HIGH CORRELATION

Distinct36
Distinct (%)29.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.975403226
Minimum1.21
Maximum5.51
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2022-09-19T22:22:36.698752image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1.21
5-th percentile1.5165
Q12.21
median3
Q33.6
95-th percentile4.48
Maximum5.51
Range4.3
Interquartile range (IQR)1.39

Descriptive statistics

Standard deviation0.9600536902
Coefficient of variation (CV)0.3226633896
Kurtosis-0.3568581322
Mean2.975403226
Median Absolute Deviation (MAD)0.71
Skewness0.297490911
Sum368.95
Variance0.9217030881
MonotonicityNot monotonic
2022-09-19T22:22:37.040815image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
3.419
 
7.3%
27
 
5.6%
2.87
 
5.6%
3.217
 
5.6%
2.716
 
4.8%
1.716
 
4.8%
3.16
 
4.8%
4.115
 
4.0%
2.35
 
4.0%
2.214
 
3.2%
Other values (26)62
50.0%
ValueCountFrequency (%)
1.211
 
0.8%
1.31
 
0.8%
1.43
2.4%
1.52
 
1.6%
1.612
 
1.6%
1.716
4.8%
1.82
 
1.6%
1.93
2.4%
27
5.6%
2.13
2.4%
ValueCountFrequency (%)
5.512
 
1.6%
5.211
 
0.8%
5.111
 
0.8%
4.712
 
1.6%
4.511
 
0.8%
4.314
3.2%
4.115
4.0%
4.014
3.2%
3.914
3.2%
3.83
2.4%

FFM of Left Arm
Real number (ℝ≥0)

HIGH CORRELATION

Distinct41
Distinct (%)33.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.944677419
Minimum1.11
Maximum5.41
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2022-09-19T22:22:37.497161image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1.11
5-th percentile1.6015
Q12.1
median2.91
Q33.6
95-th percentile4.68
Maximum5.41
Range4.3
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation0.9894561498
Coefficient of variation (CV)0.3360151245
Kurtosis-0.4966145604
Mean2.944677419
Median Absolute Deviation (MAD)0.8
Skewness0.3236465716
Sum365.14
Variance0.9790234723
MonotonicityNot monotonic
2022-09-19T22:22:38.029292image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
3.67
 
5.6%
3.416
 
4.8%
3.36
 
4.8%
2.56
 
4.8%
26
 
4.8%
2.716
 
4.8%
3.85
 
4.0%
35
 
4.0%
1.85
 
4.0%
2.85
 
4.0%
Other values (31)67
54.0%
ValueCountFrequency (%)
1.111
 
0.8%
1.211
 
0.8%
1.31
 
0.8%
1.42
 
1.6%
1.51
 
0.8%
1.61
 
0.8%
1.614
3.2%
1.714
3.2%
1.85
4.0%
1.93
2.4%
ValueCountFrequency (%)
5.411
 
0.8%
5.312
1.6%
5.211
 
0.8%
4.713
2.4%
4.511
 
0.8%
4.412
1.6%
4.314
3.2%
4.211
 
0.8%
4.111
 
0.8%
4.012
1.6%

FFM of Right Leg
Real number (ℝ≥0)

HIGH CORRELATION

Distinct58
Distinct (%)46.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.629677419
Minimum3.71
Maximum11.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2022-09-19T22:22:38.464277image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum3.71
5-th percentile4.71
Q16.11
median7.91
Q38.8325
95-th percentile10.81
Maximum11.9
Range8.19
Interquartile range (IQR)2.7225

Descriptive statistics

Standard deviation1.876984234
Coefficient of variation (CV)0.2460109557
Kurtosis-0.6643720211
Mean7.629677419
Median Absolute Deviation (MAD)1.35
Skewness0.05520292296
Sum946.08
Variance3.523069814
MonotonicityNot monotonic
2022-09-19T22:22:38.967034image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.215
 
4.0%
5.715
 
4.0%
8.95
 
4.0%
8.014
 
3.2%
8.814
 
3.2%
7.914
 
3.2%
8.114
 
3.2%
6.013
 
2.4%
7.813
 
2.4%
5.013
 
2.4%
Other values (48)84
67.7%
ValueCountFrequency (%)
3.712
1.6%
4.511
 
0.8%
4.613
2.4%
4.712
1.6%
4.812
1.6%
4.911
 
0.8%
5.013
2.4%
5.213
2.4%
5.411
 
0.8%
5.613
2.4%
ValueCountFrequency (%)
11.91
0.8%
11.611
0.8%
11.41
0.8%
11.211
0.8%
11.011
0.8%
10.91
0.8%
10.812
1.6%
10.511
0.8%
10.42
1.6%
10.311
0.8%

FFM of Left Leg
Real number (ℝ≥0)

HIGH CORRELATION

Distinct57
Distinct (%)46.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.575322581
Minimum3.71
Maximum12.01
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2022-09-19T22:22:39.426975image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum3.71
5-th percentile4.725
Q16.11
median7.61
Q38.9
95-th percentile10.4935
Maximum12.01
Range8.3
Interquartile range (IQR)2.79

Descriptive statistics

Standard deviation1.873854961
Coefficient of variation (CV)0.2473630583
Kurtosis-0.6824212649
Mean7.575322581
Median Absolute Deviation (MAD)1.4
Skewness0.1130511379
Sum939.34
Variance3.511332415
MonotonicityNot monotonic
2022-09-19T22:22:39.843648image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.615
 
4.0%
8.015
 
4.0%
8.514
 
3.2%
5.914
 
3.2%
7.014
 
3.2%
4.914
 
3.2%
7.914
 
3.2%
8.814
 
3.2%
10.113
 
2.4%
5.713
 
2.4%
Other values (47)84
67.7%
ValueCountFrequency (%)
3.711
 
0.8%
3.81
 
0.8%
4.512
1.6%
4.713
2.4%
4.813
2.4%
4.914
3.2%
5.112
1.6%
5.312
1.6%
5.411
 
0.8%
5.512
1.6%
ValueCountFrequency (%)
12.011
0.8%
11.511
0.8%
11.41
0.8%
11.211
0.8%
11.111
0.8%
10.91
0.8%
10.511
0.8%
10.41
0.8%
10.312
1.6%
10.211
0.8%

FFM of Trunk
Real number (ℝ≥0)

HIGH CORRELATION

Distinct93
Distinct (%)75.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.70403226
Minimum12.9
Maximum38.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2022-09-19T22:22:40.239601image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum12.9
5-th percentile14.8
Q118.925
median23.6
Q327.35
95-th percentile33.025
Maximum38.7
Range25.8
Interquartile range (IQR)8.425

Descriptive statistics

Standard deviation5.754127528
Coefficient of variation (CV)0.2427488904
Kurtosis-0.4452799042
Mean23.70403226
Median Absolute Deviation (MAD)4.25
Skewness0.1895828967
Sum2939.3
Variance33.10998361
MonotonicityNot monotonic
2022-09-19T22:22:40.754928image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26.45
 
4.0%
16.13
 
2.4%
21.63
 
2.4%
25.33
 
2.4%
26.92
 
1.6%
13.52
 
1.6%
29.22
 
1.6%
21.12
 
1.6%
192
 
1.6%
30.82
 
1.6%
Other values (83)98
79.0%
ValueCountFrequency (%)
12.91
0.8%
13.52
1.6%
141
0.8%
14.31
0.8%
14.51
0.8%
14.82
1.6%
15.31
0.8%
15.41
0.8%
15.51
0.8%
15.61
0.8%
ValueCountFrequency (%)
38.71
0.8%
37.71
0.8%
36.61
0.8%
36.41
0.8%
341
0.8%
33.81
0.8%
33.11
0.8%
32.61
0.8%
31.52
1.6%
31.31
0.8%

MNA- SF (points)
Real number (ℝ≥0)

HIGH CORRELATION

Distinct11
Distinct (%)8.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.11290323
Minimum3
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2022-09-19T22:22:41.254478image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile6
Q111
median13
Q314
95-th percentile14
Maximum14
Range11
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.463422424
Coefficient of variation (CV)0.203371758
Kurtosis3.10448072
Mean12.11290323
Median Absolute Deviation (MAD)1
Skewness-1.773819837
Sum1502
Variance6.068450039
MonotonicityNot monotonic
2022-09-19T22:22:41.534320image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1453
42.7%
1225
20.2%
1113
 
10.5%
1312
 
9.7%
107
 
5.6%
94
 
3.2%
63
 
2.4%
82
 
1.6%
52
 
1.6%
42
 
1.6%
ValueCountFrequency (%)
31
 
0.8%
42
 
1.6%
52
 
1.6%
63
 
2.4%
82
 
1.6%
94
 
3.2%
107
 
5.6%
1113
10.5%
1225
20.2%
1312
9.7%
ValueCountFrequency (%)
1453
42.7%
1312
 
9.7%
1225
20.2%
1113
 
10.5%
107
 
5.6%
94
 
3.2%
82
 
1.6%
63
 
2.4%
52
 
1.6%
42
 
1.6%

MNA-SF status
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
normal nutritional status
90 
risk of malnutrition
34 

Length

Max length25
Median length25
Mean length23.62903226
Min length20

Characters and Unicode

Total characters2,930
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownormal nutritional status
2nd rownormal nutritional status
3rd rownormal nutritional status
4th rownormal nutritional status
5th rownormal nutritional status

Common Values

ValueCountFrequency (%)
normal nutritional status90
72.6%
risk of malnutrition34
 
27.4%

Length

2022-09-19T22:22:41.898145image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-19T22:22:42.281359image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
normal90
24.2%
nutritional90
24.2%
status90
24.2%
risk34
 
9.1%
of34
 
9.1%
malnutrition34
 
9.1%

Most occurring characters

ValueCountFrequency (%)
t428
14.6%
n338
11.5%
a304
10.4%
i282
9.6%
o248
8.5%
r248
8.5%
248
8.5%
l214
7.3%
u214
7.3%
s214
7.3%
Other values (3)192
6.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2682
91.5%
Space Separator248
 
8.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t428
16.0%
n338
12.6%
a304
11.3%
i282
10.5%
o248
9.2%
r248
9.2%
l214
8.0%
u214
8.0%
s214
8.0%
m124
 
4.6%
Other values (2)68
 
2.5%
Space Separator
ValueCountFrequency (%)
248
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2682
91.5%
Common248
 
8.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
t428
16.0%
n338
12.6%
a304
11.3%
i282
10.5%
o248
9.2%
r248
9.2%
l214
8.0%
u214
8.0%
s214
8.0%
m124
 
4.6%
Other values (2)68
 
2.5%
Common
ValueCountFrequency (%)
248
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2930
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t428
14.6%
n338
11.5%
a304
10.4%
i282
9.6%
o248
8.5%
r248
8.5%
248
8.5%
l214
7.3%
u214
7.3%
s214
7.3%
Other values (3)192
6.6%

MUST (points)
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
0
101 
1
 
9
2
 
8
3
 
6

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters124
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0101
81.5%
19
 
7.3%
28
 
6.5%
36
 
4.8%

Length

2022-09-19T22:22:42.595814image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-19T22:22:43.116379image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0101
81.5%
19
 
7.3%
28
 
6.5%
36
 
4.8%

Most occurring characters

ValueCountFrequency (%)
0101
81.5%
19
 
7.3%
28
 
6.5%
36
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number124
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0101
81.5%
19
 
7.3%
28
 
6.5%
36
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
Common124
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0101
81.5%
19
 
7.3%
28
 
6.5%
36
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII124
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0101
81.5%
19
 
7.3%
28
 
6.5%
36
 
4.8%

MUST status
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
normal nutritional status
101 
risk of malnutrition
23 

Length

Max length25
Median length25
Mean length24.07258065
Min length20

Characters and Unicode

Total characters2,985
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownormal nutritional status
2nd rownormal nutritional status
3rd rownormal nutritional status
4th rownormal nutritional status
5th rownormal nutritional status

Common Values

ValueCountFrequency (%)
normal nutritional status101
81.5%
risk of malnutrition23
 
18.5%

Length

2022-09-19T22:22:43.460233image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-19T22:22:43.950332image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
normal101
27.2%
nutritional101
27.2%
status101
27.2%
risk23
 
6.2%
of23
 
6.2%
malnutrition23
 
6.2%

Most occurring characters

ValueCountFrequency (%)
t450
15.1%
n349
11.7%
a326
10.9%
i271
9.1%
o248
8.3%
r248
8.3%
248
8.3%
l225
7.5%
u225
7.5%
s225
7.5%
Other values (3)170
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2737
91.7%
Space Separator248
 
8.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t450
16.4%
n349
12.8%
a326
11.9%
i271
9.9%
o248
9.1%
r248
9.1%
l225
8.2%
u225
8.2%
s225
8.2%
m124
 
4.5%
Other values (2)46
 
1.7%
Space Separator
ValueCountFrequency (%)
248
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2737
91.7%
Common248
 
8.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
t450
16.4%
n349
12.8%
a326
11.9%
i271
9.9%
o248
9.1%
r248
9.1%
l225
8.2%
u225
8.2%
s225
8.2%
m124
 
4.5%
Other values (2)46
 
1.7%
Common
ValueCountFrequency (%)
248
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2985
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t450
15.1%
n349
11.7%
a326
10.9%
i271
9.1%
o248
8.3%
r248
8.3%
248
8.3%
l225
7.5%
u225
7.5%
s225
7.5%
Other values (3)170
 
5.7%

NRS-2002 (points)
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2
53 
3
47 
4
14 
5
10 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters124
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row3
3rd row3
4th row2
5th row3

Common Values

ValueCountFrequency (%)
253
42.7%
347
37.9%
414
 
11.3%
510
 
8.1%

Length

2022-09-19T22:22:44.910671image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-19T22:22:45.462404image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
253
42.7%
347
37.9%
414
 
11.3%
510
 
8.1%

Most occurring characters

ValueCountFrequency (%)
253
42.7%
347
37.9%
414
 
11.3%
510
 
8.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number124
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
253
42.7%
347
37.9%
414
 
11.3%
510
 
8.1%

Most occurring scripts

ValueCountFrequency (%)
Common124
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
253
42.7%
347
37.9%
414
 
11.3%
510
 
8.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII124
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
253
42.7%
347
37.9%
414
 
11.3%
510
 
8.1%

NRS-2002 status
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
risk of malnutrition
71 
normal nutritional status
53 

Length

Max length25
Median length20
Mean length22.13709677
Min length20

Characters and Unicode

Total characters2,745
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownormal nutritional status
2nd rowrisk of malnutrition
3rd rowrisk of malnutrition
4th rownormal nutritional status
5th rowrisk of malnutrition

Common Values

ValueCountFrequency (%)
risk of malnutrition71
57.3%
normal nutritional status53
42.7%

Length

2022-09-19T22:22:45.837475image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-19T22:22:46.333111image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
risk71
19.1%
of71
19.1%
malnutrition71
19.1%
normal53
14.2%
nutritional53
14.2%
status53
14.2%

Most occurring characters

ValueCountFrequency (%)
t354
12.9%
i319
11.6%
n301
11.0%
r248
9.0%
248
9.0%
o248
9.0%
a230
8.4%
s177
6.4%
l177
6.4%
u177
6.4%
Other values (3)266
9.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2497
91.0%
Space Separator248
 
9.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t354
14.2%
i319
12.8%
n301
12.1%
r248
9.9%
o248
9.9%
a230
9.2%
s177
7.1%
l177
7.1%
u177
7.1%
m124
 
5.0%
Other values (2)142
5.7%
Space Separator
ValueCountFrequency (%)
248
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2497
91.0%
Common248
 
9.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t354
14.2%
i319
12.8%
n301
12.1%
r248
9.9%
o248
9.9%
a230
9.2%
s177
7.1%
l177
7.1%
u177
7.1%
m124
 
5.0%
Other values (2)142
5.7%
Common
ValueCountFrequency (%)
248
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2745
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t354
12.9%
i319
11.6%
n301
11.0%
r248
9.0%
248
9.0%
o248
9.0%
a230
8.4%
s177
6.4%
l177
6.4%
u177
6.4%
Other values (3)266
9.7%

presence of any etiologic criteria (GLIM )
Boolean

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size252.0 B
True
124 
ValueCountFrequency (%)
True124
100.0%
2022-09-19T22:22:46.656912image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size252.0 B
False
76 
True
48 
ValueCountFrequency (%)
False76
61.3%
True48
38.7%
2022-09-19T22:22:46.976608image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

ALM index
Real number (ℝ≥0)

HIGH CORRELATION

Distinct112
Distinct (%)90.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.51733871
Minimum4.77
Maximum10.85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2022-09-19T22:22:47.376249image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum4.77
5-th percentile5.218
Q16.4475
median7.515
Q38.4325
95-th percentile9.8945
Maximum10.85
Range6.08
Interquartile range (IQR)1.985

Descriptive statistics

Standard deviation1.438004551
Coefficient of variation (CV)0.1912917066
Kurtosis-0.5587389693
Mean7.51733871
Median Absolute Deviation (MAD)1.06
Skewness0.1445313415
Sum932.15
Variance2.067857088
MonotonicityNot monotonic
2022-09-19T22:22:47.793089image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.132
 
1.6%
6.192
 
1.6%
7.522
 
1.6%
7.362
 
1.6%
7.752
 
1.6%
7.852
 
1.6%
7.242
 
1.6%
8.722
 
1.6%
7.352
 
1.6%
7.262
 
1.6%
Other values (102)104
83.9%
ValueCountFrequency (%)
4.771
0.8%
4.821
0.8%
4.831
0.8%
5.11
0.8%
5.151
0.8%
5.161
0.8%
5.21
0.8%
5.321
0.8%
5.361
0.8%
5.381
0.8%
ValueCountFrequency (%)
10.851
0.8%
10.741
0.8%
10.581
0.8%
10.481
0.8%
10.361
0.8%
10.171
0.8%
9.921
0.8%
9.751
0.8%
9.711
0.8%
9.481
0.8%
Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size252.0 B
False
95 
True
29 
ValueCountFrequency (%)
False95
76.6%
True29
 
23.4%
2022-09-19T22:22:48.073028image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

handgrip strenght (mean value for two hands)
Real number (ℝ≥0)

HIGH CORRELATION

Distinct122
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.99326613
Minimum5.4
Maximum49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2022-09-19T22:22:48.426065image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum5.4
5-th percentile12.19875
Q118.99375
median24.2
Q332.25
95-th percentile42.8475
Maximum49
Range43.6
Interquartile range (IQR)13.25625

Descriptive statistics

Standard deviation9.489914995
Coefficient of variation (CV)0.3650912874
Kurtosis-0.4860302543
Mean25.99326613
Median Absolute Deviation (MAD)6.34
Skewness0.3591628281
Sum3223.165
Variance90.0584866
MonotonicityNot monotonic
2022-09-19T22:22:48.891212image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.752
 
1.6%
37.6252
 
1.6%
34.51
 
0.8%
33.0251
 
0.8%
23.4251
 
0.8%
21.61
 
0.8%
23.1251
 
0.8%
41.151
 
0.8%
26.2251
 
0.8%
20.7751
 
0.8%
Other values (112)112
90.3%
ValueCountFrequency (%)
5.41
0.8%
7.551
0.8%
10.41
0.8%
11.752
1.6%
11.9751
0.8%
12.151
0.8%
12.4751
0.8%
12.5751
0.8%
13.4251
0.8%
13.9251
0.8%
ValueCountFrequency (%)
491
0.8%
46.5751
0.8%
46.4251
0.8%
45.751
0.8%
44.71
0.8%
44.21
0.8%
42.91
0.8%
42.551
0.8%
41.8251
0.8%
41.751
0.8%
Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size252.0 B
False
78 
True
46 
ValueCountFrequency (%)
False78
62.9%
True46
37.1%
2022-09-19T22:22:49.306103image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Chair Stand Test (s)
Real number (ℝ≥0)

HIGH CORRELATION

Distinct123
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.8775
Minimum5.65
Maximum41.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2022-09-19T22:22:49.615671image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum5.65
5-th percentile8.2765
Q110.5525
median13.055
Q317.0925
95-th percentile28.0465
Maximum41.2
Range35.55
Interquartile range (IQR)6.54

Descriptive statistics

Standard deviation6.26917697
Coefficient of variation (CV)0.421386454
Kurtosis3.187336244
Mean14.8775
Median Absolute Deviation (MAD)3.15
Skewness1.594359248
Sum1844.81
Variance39.30257988
MonotonicityNot monotonic
2022-09-19T22:22:49.803310image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.012
 
1.6%
10.61
 
0.8%
21.71
 
0.8%
17.721
 
0.8%
14.161
 
0.8%
8.21
 
0.8%
13.31
 
0.8%
7.351
 
0.8%
11.251
 
0.8%
10.751
 
0.8%
Other values (113)113
91.1%
ValueCountFrequency (%)
5.651
0.8%
6.741
0.8%
6.881
0.8%
7.351
0.8%
7.821
0.8%
7.921
0.8%
8.21
0.8%
8.711
0.8%
8.721
0.8%
8.731
0.8%
ValueCountFrequency (%)
41.21
0.8%
37.661
0.8%
31.691
0.8%
30.191
0.8%
29.511
0.8%
29.391
0.8%
28.241
0.8%
26.951
0.8%
25.761
0.8%
24.061
0.8%
Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size252.0 B
False
82 
True
42 
ValueCountFrequency (%)
False82
66.1%
True42
33.9%
2022-09-19T22:22:50.142412image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size252.0 B
False
108 
True
16 
ValueCountFrequency (%)
False108
87.1%
True16
 
12.9%
2022-09-19T22:22:50.480570image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Interactions

2022-09-19T22:22:01.860036image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:20:37.329004image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:20:39.562685image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:20:41.533946image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:20:43.755990image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:20:46.124598image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:20:48.318979image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:20:51.384834image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:20:55.775583image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:20:59.798772image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:21:04.563484image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:21:08.749028image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:21:12.369602image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:21:16.619463image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:21:20.564072image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:21:24.951777image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:21:29.590175image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:21:36.244363image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:21:42.599308image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:21:49.575349image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:21:55.238718image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:22:03.048718image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:20:37.455840image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:20:39.654347image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:20:41.632698image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:20:43.870561image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:20:46.221502image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:20:48.416074image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:20:51.856756image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:20:56.076993image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:21:00.058058image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:21:04.696924image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:21:08.953536image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:21:12.523382image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:21:16.832639image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:21:20.755238image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:21:25.167975image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:21:29.784419image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:21:36.511366image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:21:42.849879image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:21:50.167722image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:21:55.367531image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:22:03.342661image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:20:37.556921image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:20:39.745342image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:20:41.727379image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:20:43.973886image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:20:46.315659image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:20:48.510500image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:20:52.024409image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:20:56.217982image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:21:00.186673image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:21:04.915711image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:21:09.119046image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:21:12.721101image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:21:17.069604image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:21:20.924748image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:21:25.347323image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:21:29.994660image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:21:36.780885image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:21:43.063844image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:21:50.747853image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:21:55.505268image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:22:03.706675image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:20:37.657850image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:20:39.839595image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:20:41.821803image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:20:44.080506image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:20:46.411037image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:20:48.601841image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:20:52.407492image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:20:56.382029image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:21:00.323870image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:21:05.116544image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:21:09.302806image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:21:13.147635image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:21:17.276933image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:21:21.064383image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:21:25.535984image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:21:30.127602image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:21:36.975488image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:21:43.294737image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:21:51.112832image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:21:55.618345image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:22:04.154308image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:20:37.761961image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:20:39.939919image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:20:41.925135image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:20:44.197141image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:20:46.511488image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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Correlations

2022-09-19T22:22:50.868415image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-09-19T22:22:51.676177image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-09-19T22:22:53.219343image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-09-19T22:22:54.047615image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-09-19T22:22:54.822846image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-09-19T22:22:10.985224image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-09-19T22:22:14.901040image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

subject numberagegenderplace of livingliving conditionlevel of educationnumber of chronic diseasesnumber of regular drugspresence of hypertensionpresence of cardiovascular diseasepresence of dyslipidemiapresence of diabetesAMTS (points)AMTS statusFEV1/FVC EXFEV1Severity of COPD according to the GOLD classification6MWT resultsheight (cm)weight (kg)BFM (Body Fat Mass)SMM (Skeletal Muscle Mass)BMI (Body Mass Index)PBF (Percent Body Fat)FFM of Right ArmFFM of Left ArmFFM of Right LegFFM of Left LegFFM of TrunkMNA- SF (points)MNA-SF statusMUST (points)MUST statusNRS-2002 (points)NRS-2002 statuspresence of any etiologic criteria (GLIM )presence of any phenotypic criteria (GLIM )ALM indexLow muscle mass (ALM index <=7.40 for men and <=5.6 for women; polish cut-off points)handgrip strenght (mean value for two hands)low handgrip strenght <27 kg for men and <16 kg for womenChair Stand Test (s)weak legs- Chair Stand Test >15 secondssarcopenia confirmed according to EWGSOP2
0165Mrural areasliving with othervocational35nononoyes8normal cognitive function29.2224very severe obstruction51017274.414.433.625.119.33.913.918.518.5128.714normal nutritional status0normal nutritional status2normal nutritional statusyesno8.40no34.500no10.60nono
1269Murban areasliving with othersecondary24yesnonono9normal cognitive function50.2659moderate obstruction43018187.926.334.226.830.03.213.3010.409.9026.212normal nutritional status0normal nutritional status3risk of malnutritionyesno8.09no46.575no12.85nono
2382Murban areasliving with othervocational211yesnonono9normal cognitive function47.0636severe obstruction24516979.231.526.027.739.82.802.507.417.6122.113normal nutritional status0normal nutritional status3risk of malnutritionyesyes7.11yes17.125yes15.99yesyes
3477Mrural areasliving with otherprimary64yesyesnoyes9normal cognitive function69.3650moderate obstruction38017083.225.932.028.831.13.713.718.118.0128.314normal nutritional status0normal nutritional status2normal nutritional statusyesno8.15no28.850no13.04nono
4560Murban areasliving with othervocational22nononono10normal cognitive function48.8350moderate obstructionunable to complete the 6MWT17272.119.529.324.427.03.213.107.717.7125.014normal nutritional status0normal nutritional status3risk of malnutritionyesyes7.35yes24.750yes12.94noyes
5681Mrural areasliving with othersecondary414yesyesnoyes10normal cognitive function53.6246severe obstructionunable to complete the 6MWT17694.330.035.730.431.84.314.419.2110.2131.314normal nutritional status0normal nutritional status3risk of malnutritionyesno9.08no23.675yes14.86nono
6767Murban areasliving with othervocational45yesyesnoyes10normal cognitive function44.8324very severe obstructionunable to complete the 6MWT175107.339.337.835.036.64.114.2110.4010.3130.614normal nutritional status0normal nutritional status2normal nutritional statusyesno9.48no37.050no13.07nono
7876Murban areasliving with othervocational512noyesnoyes10normal cognitive function48.5641severe obstruction12017567.521.025.122.031.12.712.716.917.0122.76risk of malnutrition2risk of malnutrition4risk of malnutritionyesyes6.31yes23.250yes31.69yesyes
8961Mrural areasliving with othervocational26yesnonono10normal cognitive function54.6846severe obstruction38017259.111.225.820.019.02.712.506.516.6121.76risk of malnutrition3risk of malnutrition2normal nutritional statusyesyes6.19yes16.200yes28.24yesyes
91063Murban areasliving with othervocational34yesyesnono9normal cognitive function30.4120very severe obstruction13017351.58.923.017.217.22.212.216.516.5119.511risk of malnutrition2risk of malnutrition3risk of malnutritionyesyes5.83yes22.600yes8.72noyes

Last rows

subject numberagegenderplace of livingliving conditionlevel of educationnumber of chronic diseasesnumber of regular drugspresence of hypertensionpresence of cardiovascular diseasepresence of dyslipidemiapresence of diabetesAMTS (points)AMTS statusFEV1/FVC EXFEV1Severity of COPD according to the GOLD classification6MWT resultsheight (cm)weight (kg)BFM (Body Fat Mass)SMM (Skeletal Muscle Mass)BMI (Body Mass Index)PBF (Percent Body Fat)FFM of Right ArmFFM of Left ArmFFM of Right LegFFM of Left LegFFM of TrunkMNA- SF (points)MNA-SF statusMUST (points)MUST statusNRS-2002 (points)NRS-2002 statuspresence of any etiologic criteria (GLIM )presence of any phenotypic criteria (GLIM )ALM indexLow muscle mass (ALM index <=7.40 for men and <=5.6 for women; polish cut-off points)handgrip strenght (mean value for two hands)low handgrip strenght <27 kg for men and <16 kg for womenChair Stand Test (s)weak legs- Chair Stand Test >15 secondssarcopenia confirmed according to EWGSOP2
11411581Wurban areasliving with othersecondary45yesnonoyes10normal cognitive function36.9630severe obstruction33015460.220.421.225.433.81.902.005.615.5117.214normal nutritional status0normal nutritional status5risk of malnutritionyesno6.33no18.275no14.39nono
11511667Wrural areasliving alonevocational48yesnonono9normal cognitive function53.4050moderate obstruction42015377.935.822.633.346.02.302.215.715.7119.014normal nutritional status0normal nutritional status4risk of malnutritionyesno6.80no15.650yes17.09yesno
11611781Wurban areasliving alonehigher413yesyesnono10normal cognitive function35.2929very severe obstruction28014254.119.917.726.836.71.711.613.713.7114.811risk of malnutrition0normal nutritional status4risk of malnutritionyesyes5.32yes16.200yes14.73noyes
11711867Murban areasliving alonevocational38yesnonoyes10normal cognitive function52.7043severe obstruction46516494.535.732.835.137.83.303.418.619.3125.814normal nutritional status0normal nutritional status2normal nutritional statusyesno9.16no30.050no8.71nono
11811982Wurban areasliving with othersecondary37yesnonono9normal cognitive function50.0056moderate obstruction36015871.828.823.028.840.02.001.906.616.3117.512normal nutritional status0normal nutritional status3risk of malnutritionyesno6.74no13.425yes12.66nono
11912077Mrural areasliving with othervocational12nononono8normal cognitive function61.7454moderate obstructionunable to complete the 6MWT17164.08.130.721.912.73.413.418.018.1125.412normal nutritional status0normal nutritional status2normal nutritional statusyesyes7.85no30.975no12.68nono
12012161Wurban areasliving with othersecondary13nononono10normal cognitive function53.1139severe obstructionunable to complete the 6MWT16262.520.622.723.833.02.102.006.116.1118.714normal nutritional status0normal nutritional status3risk of malnutritionyesno6.22no20.525no12.64nono
12112274Murban areasliving with othervocational34yesnonono10normal cognitive function53.6167moderate obstructionunable to complete the 6MWT17571.917.429.823.524.23.213.108.117.4124.814normal nutritional status0normal nutritional status3risk of malnutritionyesyes7.13yes38.475no13.87nono
12212374Wurban areasliving aloneprimary38yesnonono9normal cognitive function56.9375moderate obstructionunable to complete the 6MWT16072.529.823.028.341.12.102.006.116.6118.511risk of malnutrition0normal nutritional status2normal nutritional statusyesyes6.60no21.800no16.89yesno
12312468Wurban areasliving with othervocational42yesnonono10normal cognitive function64.3280mild obstruction58015156.219.020.024.633.92.212.104.614.5118.512normal nutritional status0normal nutritional status2normal nutritional statusyesno5.89no21.975no8.87nono